As in the example data 'intro.csv' in the MIA it has users 1-5 so if I ask
for recommendations for user 1 then this works but if I ask for
recommendations for user 6 (a new user yet to be added to the data model)
then I get no recommendations ... so if I substitute users for orders then
again I will get no recommendations ... which I sort of understand so do I
need to inject my 'new' active order; along with its attached item/s into
the data model first and then ask for the recommendations for the order by
offering up the new orderId? or is there a way of merely offering up an
'item' and then getting recommendations based merely on the item using the
data already stored and the relationships with my item?
My assumptions:
#1
I am assuming the data model is a static island of data that has been
processed (flattened) overnight (most probably by an Hadoop process) due to
the size of this data ... rather than a living document that is updated as
soon as new data is available.
#2
I'm also assuming that instead of reading in the data model and
providing recommendations 'on the fly' I will have to run thru every item
in my catalogue and find out the top 5 recommended items that are ordered
with each item (most probably via a Hadoop process) and then store this
output in dynamoDb or luncene for quick access.
Sorry for all the questions but it such an interesting subject.
On 11 April 2013 22:04, Ted Dunning ted.dunn...@gmail.com wrote:
Actually, making this user based is a really good thing because you get
recommendations from one session to the next. These may be much more
valuable for cross-sell than things in the same order.
On Thu, Apr 11, 2013 at 12:50 PM, Sean Owen sro...@gmail.com wrote:
You can try treating your orders as the 'users'. Then just compute
item-item similarities per usual.
On Thu, Apr 11, 2013 at 7:59 PM, Billy b...@ntlworld.com wrote:
Thanks for replying,
I don't have users, well I do :-) but in this case it should not
influence
the recommendations
,
these need to be based on the relationship between
items ordered with other items
in the 'same order'
.
E.g. If item #1 has been order with item #4
[
22
]
times and item #1 has been order with item #9
[
57
]
times then
if I added item #1 to my order
these would both be recommended
but item #9 would be recommended above item #4 purely based on the fact
that
the relationship between item #1 and item #9 is greater than the
relationship with item #4.
What I don't want is; if a user ordered items #A, #B, #C separately
'at some point in their order history' then recommen
d #A and #C to other users who order #B ... I still don't want this if
the
items are similar and/or the users similar.
Cheers
Billy
On 11 Apr 2013 18:28, Sean Owen sro...@gmail.com wrote:
This sounds like just a most-similar-items problem. That's good news
because that's simpler. The only question is how you want to compute
item-item similarities. That could be based on user-item interactions.
If you're on Hadoop, try the RowSimilarityJob (where you will need
rows to be items, columns the users).
On Thu, Apr 11, 2013 at 6:11 PM, Billy b...@ntlworld.com wrote:
I am very new to Mahout and currently just ready up to chapter 5 of
'MIA'
but after reading about the various User centric and Item centric
recommenders they all seem to still need a userId so still unsure if
Mahout
can help with a fairly common recommendation.
My requirement is to produce 'n' item recommendations based on a
chosen
item.
E.g. if I've added item #1 to my order then based on all the
other items; in all the other orders for this site, what are the
likely items that I may also want add to my order based; on the item
to
item relationship in the history of orders of this site?
Most probably using the most popular relationship between the item I
have
chosen and all the items in all the other orders.
My data is not 'user' specific; and I don't think it should be, but
more
like order specific as its the pattern of items in each order that
should
determine the recommendation.
I have no preference values so merely boolean preferences will be
used.
If Mahout can perform these calculations then how must I present the
data?
Will I need to shape the data in some way to feed into Mahout
(currently
versed in using Hadoop via Aws Emr using Java)
Thanks for the advice in advance,
Billy
